Vector databases

Vector databases store embeddings: numeric representations of the meaning of text, images or products. They let software find items that are similar in meaning rather than matching exact words, which is the foundation of AI chatbots that answer from your documents and of smart recommendations.

How we use Vector databases

• Building knowledge-base chatbots that retrieve the right passages before answering (RAG).
• Adding semantic search to websites, help centres and product catalogues.
• Creating “related items” and recommendation features.
• Choosing between pgvector in PostgreSQL and dedicated services such as Pinecone or Qdrant.

When Vector databases is the right choice

Use a vector database when users search by meaning, or when an AI assistant must answer from your own content accurately. For a few hundred documents, pgvector inside your existing database is usually enough; larger collections may justify a dedicated service.

Related: AI development, LangChain, PostgreSQL. Talk to our team about your project.

Frequently asked questions

Is my company data used to train the AI model?

Storing documents in a vector database does not train the model; it lets the application look up relevant passages at question time. We also choose API settings that respect your data-usage requirements.

How do you keep chatbot answers accurate?

We retrieve only relevant, up-to-date passages, ask the model to answer from them and show sources where useful, then test with real questions before launch.